Iterative Determination of Local Bound Constraints in Iterative Image Restoration

In this paper, the problem of how to better estimate spatially adaptive intensity bounds for image restoration is addressed. When the intensity bounds are estimated from a degraded image, blurring leads to underestimation of the bounds in the edge and texture regions. Therefore, an iterative implementation of the restoration algorithm has been proposed in which the intensity bounds are re-estimated from the current image estimate. However, direct update of the bounds leads to over-smoothing in regions where the bounds are active. Furthermore, the resulting algorithm exhibits slow convergence. In this paper, alternative methods of initially estimating and updating the bounds are proposed, and the results for the xedand updated-bound implementations are compared. A method for estimation of the bound tightness parameter is also proposed.